Robust regression and outlier detection
Robust regression and outlier detection
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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Pattern Recognition Letters
A fuzzy logic-based method for outliers detection
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
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Signal Processing
TOCSAC: TOpology Constraint SAmple Consensus for Fast and Reliable Feature Correspondence
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Estimation of the parameters in regularized simultaneous super-resolution
Pattern Recognition Letters
A probabilistic approach for the simultaneous mammogram registration and abnormality detection
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Combining registration and abnormality detection in mammography
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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We address the question of how to characterize the outliers that may appear when matching two views of the same scene. The match is performed by comparing the difference of the two views at a pixel level aiming at a better registration of the images. When using digital photographs as input, we notice that an outlier is often a region that has been occluded, an object that suddenly appears in one of the images, or a region that undergoes an unexpected motion. By assuming that the error in pixel intensity generated by the outlier is similar to an error generated by comparing two random regions in the scene, we can build a model for the outliers based on the content of the two views. We illustrate our model by solving a pose estimation problem: the goal is to compute the camera motion between two views. The matching is expressed as a mixture of inliers versus outliers, and defines a function to minimize for improving the pose estimation. Our model has two benefits: First, it delivers a probability for each pixel to belong to the outliers. Second, our tests show that the method is substantially more robust than traditional robust estimators (M -estimators) used in image stitching applications, with only a slightly higher computational complexity.